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Type 2 diabetes (T2D) prevalence in adults at a younger age has increased but the disease status may go unnoticed. This study aimed to determine whether the onset age and subsequent diabetic complications can be attributed to the polygenic architecture of T2D in the Taiwan Han population. A total of 9,627 cases with T2D and 85,606 controls from the Taiwan Biobank were enrolled. Three diabetic polygenic risk scores (PRSs), PRS_EAS and PRS_EUR, and a trans-ancestry PRS (PRS_META), calculated using summary statistic from East Asian and European populations. The onset age was identified by linking to the National Taiwan Insurance Research Database, and the incidence of different diabetic complications during follow-up was recorded. PRS_META (7.4%) explained a higher variation for T2D status. And the higher percentile of PRS is also correlated with higher percentage of T2D family history and prediabetes status. More, the PRS was negatively associated with onset age (β = −0.91 yr), and this was more evident among males (β = −1.11 vs. −0.76 for males and females, respectively). The hazard ratio of diabetic retinopathy (DR) and diabetic foot were significantly associated with PRS_EAS and PRS_META, respectively. However, the PRS was not associated with other diabetic complications, including diabetic nephropathy, cardiovascular disease, and hypertension. Our findings indicated that diabetic PRS which combined susceptibility variants from cross-population could be used as a tool for early screening of T2D, especially for high-risk populations, such as individuals with high genetic risk, and may be associated with the risk of complications in subjects with T2D. NEW & NOTEWORTHY: Our findings indicated that diabetic polygenic risk score (PRS) which combined susceptibility variants from Asian and European population affect the onset age of type 2 diabetes (T2D) and could be used as a tool for early screening of T2D, especially for individuals with high genetic risk, and may be associated with the risk of diabetic complications among people in Taiwan. [ABSTRACT FROM AUTHOR]

(1) Background: This study aimed to test the feasibility of utilizing the screening tool for fall risk assessment in adult inpatient and verify its accuracy in a medical center in Taiwan. (2) Methods: This study retrospectively collected all adult fall cases among inpatients occurring in the general wards of a medical center between 1 January 2013 and 31 December 2015. This inpatient fall risk screening scale was measured by the sensitivity, specificity, and accuracy. (3) Results: There were 1331 (0.4%) falls among a total of 357,395 inpatients during this period. Factors predictive of falling risk included: age, consciousness, body shift assistance, use of fall risk medications, fall history, dizziness or weakness, toileting, and impaired mobility. Using the eight-factor assessment, two was the best cutoff point for identifying the fall risk group, with area under Receiver Operating Characteristic (ROC) curve (AUC) = 0.817, sensitivity = 80.93%, specificity = 73.0%, accuracy = 73.03%, and likelihood ratio = 11.48. (4) Conclusions: The accuracy of the eight-item fall risk assessment tool created for this study was validated. These results can serve as a reference for institutions to develop more effective fall risk assessment scale for inpatients, enabling clinical nurses to identify and more comprehensively assess the groups at highest risk for falling during their hospital stay. [ABSTRACT FROM AUTHOR]

Purpose This study used the Criteria for Distinguishing Abuse from Accidents Chinese version (CDAA-C) to determine cases of child abuse, undetermined intent, and unintentional injury, and compared risk factors and injury profiles among hospitalized children in Taiwan. Design A retrospective chart review study. Methods We selected children aged 0–3 who were hospitalized with a discharge diagnosis of injury or child maltreatment in a medical center in southern Taiwan between 2007 and 2009. The CDAA-C was used to collect children's demographics and injury types and to determine the intent of injury. Children's charts with records and notes of admission, surgery and emergency care were reviewed and analyzed. Results Among 157 eligible cases, the CDAA-C identified 13 child abuse cases including additional 8 victims who were not previously coded in ICD-9-CM. Most abused children suffered from brain injuries, had ≥ 2 in jury incidents, and had a misdiagnosis in the previous visit. Abused children were younger and more likely to be born premature. Conclusions This study highlighted the under-diagnosis of child abuse in Taiwan. An easy-to-use standardized screening tool for child abuse in Taiwan is needed. [ABSTRACT FROM AUTHOR]

Purpose To screen health literacy among urban elderly in Taiwan, who cannot be evaluated easily using the current measurement tools because of the “face”, which meant someone felt embarrassed if he did not know how to do something. Materials and methods A literature review was performed to define a framework for developing the health literacy screening tool. Two hundred elderly were recruited to test the validity and reliability for pilot study. One thousand and eighty two elderly who came from quota sampling in Taipei City by administrative areas and gender were interviewed face-to-face to gather health literacy performance by the developed health literacy screening tool and the short-form Mandarin Health Literacy Scale (s-MHLS). Results 10-items of health literacy screening tool by self-perception were developed. The mean score of screening tool among analysis sample was 42.3 (0–50) and s-MHLS was 9.5 (0–11). Pearson correlation coefficient was 0.441 ( p < 0.0001) between these two measurements. Multiple regressions showed that, female, younger, higher education, living with family, has no primary caregiver, has few medical companionship, and higher score of health knowledge had better health literacy performance in both measurements. Conclusions This screening tool should be applied to screen health literacy of elderly came from baby boomer who usually have lower education levels than the general population in Chinese regions. [ABSTRACT FROM AUTHOR]

Objective To study the efficacy of the WINROP (https://winrop.com) algorithm in Taiwan, a middle income, moderately developed country in Asia. Study Design We enrolled all preterm infants born with a gestational age less than 32þ0 weeks from September 2008 to August 2010. The results of serial retinopathy of prematurity examinations according to the screening guidelines in our hospital were recorded. Weekly body weight was also recorded for the WINROP algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value were calculated. Results Overall, 148 infants were included. Seventeen infants (11.5%) received treatment for retinopathy of prematurity. But, six infants (35.3%) were missed when severe retinopathy of prematurity was predicted with WINROP algorithm. The sensitivity is only 64.7%. However, when focusing on the preterm infants with a birth weight < 1,000 g or gestational age < 28 weeks, it could predict the need for treatment up to 13 weeks in advance. Conclusion The WINROP algorithm is a very effective noninvasive screening tool for retinopathy of prematurity, especially in preterm infants with a birth weight < 1,000 g or a gestational age < 28 weeks. However, the overall sensitivity in this report from Taiwan was not as high as that reported in highly developed countries. [ABSTRACT FROM AUTHOR]

Abstract: Background: Depression generates a remarkable disease burden. Early onset of depression in young people is associated with a poor prognosis. This has precipitated developing a screening instrument for early detection of depression in Taiwan adolescents. Methods: We recruited 662 junior high school students who completed the Screening Test for Depression (STD) designed using diagnostic and statistical manual-IV diagnostic criteria of major depressive disorder for assessing depressive symptoms. The students were then interviewed by psychiatrists who used the Mini International Neuropsychiatric Interview-Kid to verify the validity of the soon-to-be-developed Rapid STD (RSTD). Multiple logistic regression analysis of the STD results was used to extract items for the RSTD. Results: We extracted four items for the RSTD: “insomnia or hypersomnia”, “recurrent thoughts of death or recurrent suicidal ideation”, “feelings of worthlessness or excessive or inappropriate guilt”, and “psychomotor agitation or retardation”. Any two of the first three yielded the best-balanced algorithm for major depressive disorder, which had a sensitivity of 75.0%, specificity of 92.9%, positive predictive value of 28.6%, and negative predictive value of 99.0%. Any two of the four yielded the best-balanced algorithm for depressive disorders, which had a sensitivity of 71.4%, specificity of 92.0%, the positive predictive value of 33.3%, and the negative predictive value of 98.3%. Conclusion: The RSTD, a 4-item tool for junior high school children, can be easily used to assess fluctuating risks of major depressive disorder and depressive disorders at any time. [Copyright &y& Elsevier]

Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve as screening tools for osteoporosis in adults over fifty years old. Additionally, we also compared the performance of newly developed models with traditional prediction models. Data were acquired from community-dwelling participants enrolled in health checkup programs at a medical center in Taiwan. A total of 3053 men and 2929 women were included. Models were constructed for men and women separately with artificial neural network (ANN), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression (LoR) to predict the presence of osteoporosis. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. We achieved AUROC of 0.837, 0.840, 0.843, 0.821, 0.827 in men, and 0.781, 0.807, 0.811, 0.767, 0.772 in women, for ANN, SVM, RF, KNN, and LoR models, respectively. The ANN, SVM, RF, and LoR models in men, and the ANN, SVM, and RF models in women performed significantly better than the traditional Osteoporosis Self-Assessment Tool for Asians (OSTA) model. We have demonstrated that machine learning algorithms improve the performance of screening for osteoporosis. By incorporating the models in clinical practice, patients could potentially benefit from earlier diagnosis and treatment of osteoporosis.

Background/purpose: Very few studies have investigated the screening tools that aim to identify the need of palliative care services among patients with advanced cancer or chronic non-malignant diseases. This study validated the one-page Taiwanese version-Palliative Care Screening Tool (TW-PCST) for screening inpatients with potential palliative care needs., Method: ROC curves were produced to examine the sensitivities and specificities at varying cut-off points. The optimal cut-off value to predict mortality was justified using the Youden's index. The screening was conducted on the first day of admission. Patients were prospectively followed-up after the baseline assessment. Three followed-up periods, namely 14 days, 90 days, and 180 days were analyzed., Results: A total of 21,596 patients were screened. AUCs for all cut-off scores varied from 0.84 to 0.88. A total-ABCD score ≥2 gave the highest Youden's index for 90 days and 180 days follow-up periods. The optimal cut-point for 14 days was score ≥3., Conclusion: The TW-PCST demonstrated a good sensitivity and specificity in identification of inpatients with palliative care needs. A total-ABCD score ≥2 may be considered as a trigger for further referral., (Copyright © 2018. Published by Elsevier B.V.)